Insight Analysis of Promiscuous Estrogen Receptor α‑Ligand Binding by a Novel Machine Learning Scheme

Estrogen
receptor α (ERα) plays a significant role
in occurrence of breast cancer and may cause various adverse side-effects
when ERα is an off-target protein. A theoretical model was derived
to predict the binding affinity of ERα using the pharmacophore
ensemble/support vector machine (PhE/SVM) scheme to consider the promiscuous
characteristic of ERα. The estimations by PhE/SVM were discovered
to be in good agreement with the observed values for those training
molecules (n = 31, r2 = 0.80, qCV2 = 0.77, RMSE = 0.57, s =
0.58), test molecules (n = 179, q2 = 0.91–0.96, RMSE = 0.33, s =
0.26) and outliers (n = 15, q2 = 0.80–0.86, RMSE = 0.56, s = 0.49).
When subjected to various statistical validations, the PhE/SVM model
consistently fulfilled the strictest criteria. A mock test also asserted
its predictivity. When compared with crystal structures, the calculated
results are consistent with the reported ERα-ligand co-complex
structure, and the plasticity nature of ERα is also disclosed.
Consequently, this precise, fast, and robust model can be adopted
to predict ERα-ligand binding affinities and to design safer
non-ERα-targeted pharmaceuticals in the process of drug discovery
and development.